5 Main figures in the paper
- We firstly provide estimations and figures used in the main text.
- These chunks are copied and pasted from subsequent outcome-based result sections.
- Actual graphs and tables in the paper are generated and saved in the subsequent chunks, not the chunks in this section. But they are identical.
5.1 WLS, with trends, Figure 6 (a) & Table C.7 (1)
- Y=PA recipients(YOY), without covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.619
## (0.762)
## treat_var:date_2018_03 -0.870
## (0.935)
## treat_var:date_2018_04 -0.788
## (1.165)
## treat_var:date_2018_05 -0.177
## (1.059)
## treat_var:date_2018_06 -0.767
## (1.400)
## treat_var:date_2018_07 0.661
## (1.207)
## treat_var:date_2018_08 0.564
## (1.558)
## treat_var:date_2018_09 -0.789
## (1.312)
## treat_var:date_2018_10 0.190
## (1.378)
## treat_var:date_2018_11 0.706
## (1.797)
## treat_var:date_2018_12 -0.352
## (1.844)
## treat_var:date_2019_01 0.559
## (1.616)
## treat_var:date_2019_02 -0.332
## (1.609)
## treat_var:date_2019_03 -1.125
## (1.534)
## treat_var:date_2019_04 -0.082
## (1.691)
## treat_var:date_2019_05 -0.215
## (1.582)
## treat_var:date_2019_06 0.430
## (1.337)
## treat_var:date_2019_07 -0.377
## (1.377)
## treat_var:date_2019_08 -1.143
## (1.295)
## treat_var:date_2019_09 -0.416
## (1.013)
## treat_var:date_2019_10 -0.288
## (1.004)
## treat_var:date_2019_11 -2.043 *
## (0.912)
## treat_var:date_2019_12 0.090
## (0.817)
## treat_var:date_2020_02 2.108 *
## (0.835)
## treat_var:date_2020_03 4.612 **
## (1.393)
## treat_var:date_2020_04 6.788 **
## (2.041)
## treat_var:date_2020_05 9.448 ***
## (2.273)
## treat_var:date_2020_06 13.654 ***
## (3.194)
## treat_var:date_2020_07 13.694 ***
## (3.750)
## treat_var:date_2020_08 14.134 **
## (4.153)
## treat_var:date_2020_09 15.521 **
## (4.700)
## as.factor(id)1:year_month_id -0.169
## (0.182)
## as.factor(id)2:year_month_id -0.752 ***
## (0.107)
## as.factor(id)3:year_month_id -0.649 ***
## (0.120)
## as.factor(id)4:year_month_id -0.755 ***
## (0.142)
## as.factor(id)5:year_month_id -0.951 ***
## (0.137)
## as.factor(id)6:year_month_id -0.696 ***
## (0.130)
## as.factor(id)7:year_month_id -1.086 ***
## (0.123)
## as.factor(id)8:year_month_id -1.100 ***
## (0.076)
## as.factor(id)9:year_month_id 0.284 ***
## (0.080)
## as.factor(id)10:year_month_id -0.208 ***
## (0.058)
## as.factor(id)11:year_month_id -0.319
## (0.166)
## as.factor(id)12:year_month_id -0.675 ***
## (0.159)
## as.factor(id)13:year_month_id -0.718 ***
## (0.175)
## as.factor(id)14:year_month_id -0.654 **
## (0.220)
## as.factor(id)15:year_month_id -0.763 ***
## (0.100)
## as.factor(id)16:year_month_id -0.182
## (0.110)
## as.factor(id)17:year_month_id -0.040
## (0.089)
## as.factor(id)18:year_month_id 0.284 ***
## (0.063)
## as.factor(id)19:year_month_id -0.634 ***
## (0.096)
## as.factor(id)20:year_month_id -0.119
## (0.061)
## as.factor(id)21:year_month_id -0.178 *
## (0.069)
## as.factor(id)22:year_month_id 0.041
## (0.083)
## as.factor(id)23:year_month_id 0.198 *
## (0.093)
## as.factor(id)24:year_month_id 0.193 *
## (0.095)
## as.factor(id)25:year_month_id -0.111
## (0.087)
## as.factor(id)26:year_month_id -0.578 ***
## (0.147)
## as.factor(id)27:year_month_id -0.655 **
## (0.213)
## as.factor(id)28:year_month_id -0.723 ***
## (0.166)
## as.factor(id)29:year_month_id -2.045 ***
## (0.192)
## as.factor(id)30:year_month_id -1.778 ***
## (0.188)
## as.factor(id)31:year_month_id -1.005 ***
## (0.125)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -0.421 ***
## (0.089)
## as.factor(id)34:year_month_id 0.332 **
## (0.096)
## as.factor(id)35:year_month_id -0.434 ***
## (0.107)
## as.factor(id)36:year_month_id -0.139
## (0.119)
## as.factor(id)37:year_month_id -0.616 ***
## (0.113)
## as.factor(id)38:year_month_id -0.398 ***
## (0.105)
## as.factor(id)39:year_month_id -0.118
## (0.087)
## as.factor(id)40:year_month_id 0.065
## (0.124)
## as.factor(id)41:year_month_id -0.215 ***
## (0.025)
## as.factor(id)42:year_month_id -0.532 ***
## (0.088)
## as.factor(id)43:year_month_id 0.766 ***
## (0.088)
## as.factor(id)44:year_month_id 0.373 **
## (0.128)
## as.factor(id)45:year_month_id 0.098
## (0.089)
## as.factor(id)46:year_month_id 0.380 *
## (0.143)
## as.factor(id)47:year_month_id -0.352
## (0.230)
## -------------------------------------------
## R^2 0.961
## Adj. R^2 0.957
## Num. obs. 1551
## RMSE 222.438
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_trend")
ggplotly(graph_yoy_hogo_persons_WLS_trend)## Warning: `group_by_()` is deprecated as of dplyr 0.7.0.
## Please use `group_by()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.
5.2 WLS, with trends, Figure 6 (b) & Table C.8 (1)
- Y=PA recipients(YOY), with covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_persons_receive,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.619
## (0.780)
## treat_var:date_2018_03 -0.871
## (0.957)
## treat_var:date_2018_04 -0.788
## (1.193)
## treat_var:date_2018_05 -0.177
## (1.084)
## treat_var:date_2018_06 -0.767
## (1.434)
## treat_var:date_2018_07 0.661
## (1.236)
## treat_var:date_2018_08 0.564
## (1.596)
## treat_var:date_2018_09 -0.789
## (1.344)
## treat_var:date_2018_10 0.190
## (1.411)
## treat_var:date_2018_11 0.706
## (1.839)
## treat_var:date_2018_12 -0.352
## (1.887)
## treat_var:date_2019_01 0.559
## (1.655)
## treat_var:date_2019_02 -0.332
## (1.649)
## treat_var:date_2019_03 -1.126
## (1.572)
## treat_var:date_2019_04 -0.083
## (1.733)
## treat_var:date_2019_05 -0.215
## (1.621)
## treat_var:date_2019_06 0.429
## (1.370)
## treat_var:date_2019_07 -0.377
## (1.411)
## treat_var:date_2019_08 -1.143
## (1.327)
## treat_var:date_2019_09 -0.416
## (1.038)
## treat_var:date_2019_10 -0.288
## (1.030)
## treat_var:date_2019_11 -2.044 *
## (0.937)
## treat_var:date_2019_12 0.089
## (0.838)
## treat_var:date_2020_02 0.651
## (2.259)
## treat_var:date_2020_03 2.787
## (2.787)
## treat_var:date_2020_04 4.756
## (4.151)
## treat_var:date_2020_05 4.305
## (4.469)
## treat_var:date_2020_06 6.808
## (5.313)
## treat_var:date_2020_07 9.076
## (5.501)
## treat_var:date_2020_08 8.261
## (5.963)
## treat_var:date_2020_09 11.639
## (6.155)
## date_2020_02:google_mobility_index_2020may 0.145
## (0.608)
## date_2020_03:google_mobility_index_2020may 0.055
## (0.751)
## date_2020_04:google_mobility_index_2020may -0.100
## (0.912)
## date_2020_05:google_mobility_index_2020may -0.545
## (0.915)
## date_2020_06:google_mobility_index_2020may -1.013
## (1.090)
## date_2020_07:google_mobility_index_2020may -1.260
## (1.105)
## date_2020_08:google_mobility_index_2020may -1.768
## (1.137)
## date_2020_09:google_mobility_index_2020may -2.016
## (1.234)
## date_2020_02:infection_rate_cumulative2020jun 0.455
## (0.324)
## date_2020_03:infection_rate_cumulative2020jun 0.276
## (0.408)
## date_2020_04:infection_rate_cumulative2020jun 0.369
## (0.433)
## date_2020_05:infection_rate_cumulative2020jun 0.113
## (0.541)
## date_2020_06:infection_rate_cumulative2020jun -0.061
## (0.591)
## date_2020_07:infection_rate_cumulative2020jun -0.290
## (0.620)
## date_2020_08:infection_rate_cumulative2020jun -0.493
## (0.662)
## date_2020_09:infection_rate_cumulative2020jun -0.652
## (0.633)
## date_2020_02:death_rate_cumulative2020jun -4.090
## (3.369)
## date_2020_03:death_rate_cumulative2020jun -3.024
## (4.346)
## date_2020_04:death_rate_cumulative2020jun -3.796
## (4.612)
## date_2020_05:death_rate_cumulative2020jun -2.399
## (5.673)
## date_2020_06:death_rate_cumulative2020jun -0.807
## (6.375)
## date_2020_07:death_rate_cumulative2020jun 3.007
## (6.751)
## date_2020_08:death_rate_cumulative2020jun 4.293
## (7.103)
## date_2020_09:death_rate_cumulative2020jun 6.049
## (6.947)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area -0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area -0.000
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio 86.794 *
## (40.699)
## date_2020_03:Secondary_industry_ratio 95.671
## (49.018)
## date_2020_04:Secondary_industry_ratio 108.190
## (58.290)
## date_2020_05:Secondary_industry_ratio 129.267 *
## (59.121)
## date_2020_06:Secondary_industry_ratio 179.775 *
## (68.263)
## date_2020_07:Secondary_industry_ratio 205.605 **
## (70.939)
## date_2020_08:Secondary_industry_ratio 235.358 **
## (72.274)
## date_2020_09:Secondary_industry_ratio 266.150 **
## (83.287)
## date_2020_02:Tertiary_industry_ratio 61.656
## (53.426)
## date_2020_03:Tertiary_industry_ratio 70.341
## (71.045)
## date_2020_04:Tertiary_industry_ratio 60.358
## (75.393)
## date_2020_05:Tertiary_industry_ratio 104.078
## (86.567)
## date_2020_06:Tertiary_industry_ratio 130.846
## (97.991)
## date_2020_07:Tertiary_industry_ratio 100.597
## (99.589)
## date_2020_08:Tertiary_industry_ratio 127.149
## (101.559)
## date_2020_09:Tertiary_industry_ratio 122.783
## (100.486)
## date_2020_02:Total_population 0.005
## (0.004)
## date_2020_03:Total_population 0.007
## (0.006)
## date_2020_04:Total_population 0.010
## (0.007)
## date_2020_05:Total_population 0.007
## (0.007)
## date_2020_06:Total_population 0.012
## (0.009)
## date_2020_07:Total_population 0.011
## (0.010)
## date_2020_08:Total_population 0.012
## (0.010)
## date_2020_09:Total_population 0.015
## (0.011)
## date_2020_02:Ratio_of_aged_population 0.011
## (0.276)
## date_2020_03:Ratio_of_aged_population 0.108
## (0.345)
## date_2020_04:Ratio_of_aged_population 0.024
## (0.416)
## date_2020_05:Ratio_of_aged_population -0.060
## (0.433)
## date_2020_06:Ratio_of_aged_population 0.213
## (0.489)
## date_2020_07:Ratio_of_aged_population 0.384
## (0.500)
## date_2020_08:Ratio_of_aged_population 0.609
## (0.511)
## date_2020_09:Ratio_of_aged_population 0.847
## (0.565)
## as.factor(id)1:year_month_id 0.725 ***
## (0.179)
## as.factor(id)2:year_month_id 0.427 ***
## (0.101)
## as.factor(id)3:year_month_id 0.284 ***
## (0.070)
## as.factor(id)4:year_month_id -0.099
## (0.167)
## as.factor(id)5:year_month_id -0.038
## (0.145)
## as.factor(id)6:year_month_id 0.078
## (0.180)
## as.factor(id)7:year_month_id -0.431 *
## (0.165)
## as.factor(id)8:year_month_id -0.593 ***
## (0.151)
## as.factor(id)9:year_month_id 0.781 ***
## (0.163)
## as.factor(id)10:year_month_id 0.172
## (0.199)
## as.factor(id)11:year_month_id 0.200
## (0.160)
## as.factor(id)12:year_month_id -0.141
## (0.226)
## as.factor(id)13:year_month_id -0.384 *
## (0.185)
## as.factor(id)14:year_month_id -0.274
## (0.176)
## as.factor(id)15:year_month_id -0.204
## (0.146)
## as.factor(id)16:year_month_id 0.145
## (0.254)
## as.factor(id)17:year_month_id 0.428
## (0.237)
## as.factor(id)18:year_month_id 0.770 ***
## (0.196)
## as.factor(id)19:year_month_id -0.109
## (0.229)
## as.factor(id)20:year_month_id 0.371
## (0.228)
## as.factor(id)21:year_month_id 0.116
## (0.208)
## as.factor(id)22:year_month_id 0.289
## (0.216)
## as.factor(id)23:year_month_id 0.388 *
## (0.188)
## as.factor(id)24:year_month_id 0.591 **
## (0.185)
## as.factor(id)25:year_month_id 0.312
## (0.198)
## as.factor(id)26:year_month_id 0.075
## (0.223)
## as.factor(id)27:year_month_id -0.191
## (0.173)
## as.factor(id)28:year_month_id -0.285
## (0.196)
## as.factor(id)29:year_month_id -1.345 ***
## (0.194)
## as.factor(id)30:year_month_id -0.828 ***
## (0.154)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 0.660 **
## (0.215)
## as.factor(id)33:year_month_id 0.209
## (0.111)
## as.factor(id)34:year_month_id 0.783 ***
## (0.191)
## as.factor(id)35:year_month_id 0.114
## (0.202)
## as.factor(id)36:year_month_id 0.654 ***
## (0.155)
## as.factor(id)37:year_month_id -0.010
## (0.175)
## as.factor(id)38:year_month_id 0.432 ***
## (0.093)
## as.factor(id)39:year_month_id 0.894 ***
## (0.140)
## as.factor(id)40:year_month_id 0.633 **
## (0.216)
## as.factor(id)41:year_month_id 0.549 ***
## (0.132)
## as.factor(id)42:year_month_id 0.325 *
## (0.122)
## as.factor(id)43:year_month_id 1.641 ***
## (0.082)
## as.factor(id)44:year_month_id 1.241 ***
## (0.096)
## as.factor(id)45:year_month_id 1.075 ***
## (0.048)
## as.factor(id)46:year_month_id 1.359 ***
## (0.054)
## as.factor(id)47:year_month_id 0.762 **
## (0.260)
## --------------------------------------------------------------------
## R^2 0.972
## Adj. R^2 0.968
## Num. obs. 1551
## RMSE 192.410
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_hogo_persons_WLS_trend")
# Event study graph
graph_yoy_hogo_persons_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_persons_WLS_trend")
ggplotly(graph_yoy_hogo_persons_WLS_trend_covar)5.3 WLS, with trends, Figure 6 (c) & Table C.7 (3)
- Y=PA recipient households(YOY), without covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ===========================================
## Model 1
## -------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.494
## (0.604)
## treat_var:date_2018_03 -0.193
## (0.665)
## treat_var:date_2018_04 -0.193
## (0.792)
## treat_var:date_2018_05 0.406
## (0.797)
## treat_var:date_2018_06 -1.078
## (0.923)
## treat_var:date_2018_07 -0.043
## (1.038)
## treat_var:date_2018_08 0.181
## (1.334)
## treat_var:date_2018_09 -1.418
## (1.071)
## treat_var:date_2018_10 -0.417
## (1.218)
## treat_var:date_2018_11 0.019
## (1.383)
## treat_var:date_2018_12 -0.644
## (1.400)
## treat_var:date_2019_01 -0.516
## (1.426)
## treat_var:date_2019_02 -1.291
## (1.323)
## treat_var:date_2019_03 -2.166
## (1.308)
## treat_var:date_2019_04 -1.643
## (1.278)
## treat_var:date_2019_05 -2.003
## (1.252)
## treat_var:date_2019_06 -0.918
## (1.019)
## treat_var:date_2019_07 -1.070
## (1.135)
## treat_var:date_2019_08 -1.643
## (0.987)
## treat_var:date_2019_09 -0.530
## (0.844)
## treat_var:date_2019_10 -0.839
## (0.728)
## treat_var:date_2019_11 -1.819 **
## (0.537)
## treat_var:date_2019_12 -0.315
## (0.449)
## treat_var:date_2020_02 1.259 *
## (0.620)
## treat_var:date_2020_03 3.171 **
## (1.094)
## treat_var:date_2020_04 5.491 ***
## (1.297)
## treat_var:date_2020_05 7.482 ***
## (1.811)
## treat_var:date_2020_06 10.349 ***
## (2.437)
## treat_var:date_2020_07 9.978 ***
## (2.789)
## treat_var:date_2020_08 10.376 ***
## (2.902)
## treat_var:date_2020_09 11.411 **
## (3.360)
## as.factor(id)1:year_month_id -0.456 **
## (0.135)
## as.factor(id)2:year_month_id -0.694 ***
## (0.079)
## as.factor(id)3:year_month_id -0.579 ***
## (0.090)
## as.factor(id)4:year_month_id -0.627 ***
## (0.106)
## as.factor(id)5:year_month_id -0.683 ***
## (0.102)
## as.factor(id)6:year_month_id -0.638 ***
## (0.097)
## as.factor(id)7:year_month_id -0.923 ***
## (0.092)
## as.factor(id)8:year_month_id -0.926 ***
## (0.056)
## as.factor(id)9:year_month_id -0.039
## (0.059)
## as.factor(id)10:year_month_id -0.467 ***
## (0.043)
## as.factor(id)11:year_month_id -0.434 **
## (0.124)
## as.factor(id)12:year_month_id -0.629 ***
## (0.119)
## as.factor(id)13:year_month_id -0.702 ***
## (0.130)
## as.factor(id)14:year_month_id -0.440 *
## (0.164)
## as.factor(id)15:year_month_id -0.655 ***
## (0.074)
## as.factor(id)16:year_month_id -0.195 *
## (0.082)
## as.factor(id)17:year_month_id 0.047
## (0.066)
## as.factor(id)18:year_month_id 0.057
## (0.047)
## as.factor(id)19:year_month_id -0.651 ***
## (0.071)
## as.factor(id)20:year_month_id -0.218 ***
## (0.045)
## as.factor(id)21:year_month_id -0.326 ***
## (0.052)
## as.factor(id)22:year_month_id -0.065
## (0.062)
## as.factor(id)23:year_month_id -0.064
## (0.070)
## as.factor(id)24:year_month_id 0.037
## (0.071)
## as.factor(id)25:year_month_id -0.144 *
## (0.065)
## as.factor(id)26:year_month_id -0.630 ***
## (0.109)
## as.factor(id)27:year_month_id -0.604 ***
## (0.158)
## as.factor(id)28:year_month_id -0.648 ***
## (0.124)
## as.factor(id)29:year_month_id -1.409 ***
## (0.143)
## as.factor(id)30:year_month_id -1.610 ***
## (0.140)
## as.factor(id)31:year_month_id -0.976 ***
## (0.093)
## as.factor(id)32:year_month_id
##
## as.factor(id)33:year_month_id -0.452 ***
## (0.066)
## as.factor(id)34:year_month_id 0.043
## (0.072)
## as.factor(id)35:year_month_id -0.608 ***
## (0.080)
## as.factor(id)36:year_month_id -0.046
## (0.089)
## as.factor(id)37:year_month_id -0.629 ***
## (0.084)
## as.factor(id)38:year_month_id -0.446 ***
## (0.078)
## as.factor(id)39:year_month_id -0.749 ***
## (0.065)
## as.factor(id)40:year_month_id -0.100
## (0.092)
## as.factor(id)41:year_month_id -0.172 ***
## (0.018)
## as.factor(id)42:year_month_id -0.777 ***
## (0.065)
## as.factor(id)43:year_month_id 0.328 ***
## (0.066)
## as.factor(id)44:year_month_id 0.283 **
## (0.095)
## as.factor(id)45:year_month_id 0.032
## (0.066)
## as.factor(id)46:year_month_id 0.129
## (0.106)
## as.factor(id)47:year_month_id -0.505 **
## (0.171)
## -------------------------------------------
## R^2 0.937
## Adj. R^2 0.930
## Num. obs. 1551
## RMSE 167.037
## N Clusters 47
## ===========================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_trend")
ggplotly(graph_yoy_hogo_households_WLS_trend)5.4 WLS, with trends, Figure 6 (d) & Table C.8 (3)
- Y=PA recipient households(YOY), with covariates
# DID estimation
estimation_results <- dynamic_DID_WLS_trend_covar8Xcovid_months(dataset = df_analysis,
outcome_var = df_analysis$yoy_households_receive,
treat_var = df_analysis$unemploy_shock_diff2)
texreg::screenreg(l = estimation_results , include.ci = FALSE, digits=3)## 2 coefficients not defined because the design matrix is rank deficient
##
## ====================================================================
## Model 1
## --------------------------------------------------------------------
## treat_var:date_2018_01
##
## treat_var:date_2018_02 0.494
## (0.618)
## treat_var:date_2018_03 -0.193
## (0.680)
## treat_var:date_2018_04 -0.194
## (0.811)
## treat_var:date_2018_05 0.405
## (0.815)
## treat_var:date_2018_06 -1.080
## (0.945)
## treat_var:date_2018_07 -0.045
## (1.062)
## treat_var:date_2018_08 0.179
## (1.365)
## treat_var:date_2018_09 -1.420
## (1.097)
## treat_var:date_2018_10 -0.419
## (1.247)
## treat_var:date_2018_11 0.017
## (1.416)
## treat_var:date_2018_12 -0.647
## (1.433)
## treat_var:date_2019_01 -0.519
## (1.459)
## treat_var:date_2019_02 -1.293
## (1.354)
## treat_var:date_2019_03 -2.169
## (1.338)
## treat_var:date_2019_04 -1.645
## (1.307)
## treat_var:date_2019_05 -2.006
## (1.281)
## treat_var:date_2019_06 -0.921
## (1.043)
## treat_var:date_2019_07 -1.073
## (1.162)
## treat_var:date_2019_08 -1.646
## (1.010)
## treat_var:date_2019_09 -0.533
## (0.863)
## treat_var:date_2019_10 -0.842
## (0.746)
## treat_var:date_2019_11 -1.822 **
## (0.552)
## treat_var:date_2019_12 -0.318
## (0.461)
## treat_var:date_2020_02 0.069
## (2.078)
## treat_var:date_2020_03 0.998
## (2.403)
## treat_var:date_2020_04 1.625
## (2.951)
## treat_var:date_2020_05 1.626
## (3.612)
## treat_var:date_2020_06 2.788
## (4.145)
## treat_var:date_2020_07 4.098
## (4.251)
## treat_var:date_2020_08 3.228
## (4.552)
## treat_var:date_2020_09 5.185
## (5.030)
## date_2020_02:google_mobility_index_2020may 0.168
## (0.425)
## date_2020_03:google_mobility_index_2020may 0.116
## (0.553)
## date_2020_04:google_mobility_index_2020may -0.003
## (0.616)
## date_2020_05:google_mobility_index_2020may -0.344
## (0.613)
## date_2020_06:google_mobility_index_2020may -0.692
## (0.725)
## date_2020_07:google_mobility_index_2020may -0.728
## (0.724)
## date_2020_08:google_mobility_index_2020may -1.104
## (0.772)
## date_2020_09:google_mobility_index_2020may -1.363
## (0.843)
## date_2020_02:infection_rate_cumulative2020jun 0.305
## (0.258)
## date_2020_03:infection_rate_cumulative2020jun 0.175
## (0.308)
## date_2020_04:infection_rate_cumulative2020jun 0.244
## (0.328)
## date_2020_05:infection_rate_cumulative2020jun 0.135
## (0.423)
## date_2020_06:infection_rate_cumulative2020jun 0.076
## (0.458)
## date_2020_07:infection_rate_cumulative2020jun -0.012
## (0.456)
## date_2020_08:infection_rate_cumulative2020jun -0.168
## (0.490)
## date_2020_09:infection_rate_cumulative2020jun -0.306
## (0.470)
## date_2020_02:death_rate_cumulative2020jun -3.140
## (2.843)
## date_2020_03:death_rate_cumulative2020jun -2.564
## (3.521)
## date_2020_04:death_rate_cumulative2020jun -3.517
## (3.714)
## date_2020_05:death_rate_cumulative2020jun -3.504
## (4.588)
## date_2020_06:death_rate_cumulative2020jun -3.292
## (5.021)
## date_2020_07:death_rate_cumulative2020jun -1.677
## (5.009)
## date_2020_08:death_rate_cumulative2020jun -0.548
## (5.275)
## date_2020_09:death_rate_cumulative2020jun 0.279
## (5.159)
## date_2020_02:Population_per_1_km_2_of_inhabitable_area 0.000
## (0.001)
## date_2020_03:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_04:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_05:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_06:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_07:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_08:Population_per_1_km_2_of_inhabitable_area 0.002
## (0.001)
## date_2020_09:Population_per_1_km_2_of_inhabitable_area 0.001
## (0.001)
## date_2020_02:Secondary_industry_ratio 74.478 *
## (29.310)
## date_2020_03:Secondary_industry_ratio 87.528 *
## (37.074)
## date_2020_04:Secondary_industry_ratio 99.933 *
## (41.162)
## date_2020_05:Secondary_industry_ratio 121.542 **
## (41.949)
## date_2020_06:Secondary_industry_ratio 156.314 **
## (48.293)
## date_2020_07:Secondary_industry_ratio 176.018 ***
## (49.784)
## date_2020_08:Secondary_industry_ratio 194.820 ***
## (53.503)
## date_2020_09:Secondary_industry_ratio 215.465 **
## (61.525)
## date_2020_02:Tertiary_industry_ratio 72.927
## (42.266)
## date_2020_03:Tertiary_industry_ratio 91.829
## (53.954)
## date_2020_04:Tertiary_industry_ratio 99.347
## (54.855)
## date_2020_05:Tertiary_industry_ratio 130.277
## (65.257)
## date_2020_06:Tertiary_industry_ratio 148.709
## (75.146)
## date_2020_07:Tertiary_industry_ratio 135.714
## (74.916)
## date_2020_08:Tertiary_industry_ratio 163.489 *
## (78.976)
## date_2020_09:Tertiary_industry_ratio 168.406 *
## (80.863)
## date_2020_02:Total_population -0.001
## (0.003)
## date_2020_03:Total_population -0.001
## (0.005)
## date_2020_04:Total_population 0.002
## (0.005)
## date_2020_05:Total_population -0.000
## (0.005)
## date_2020_06:Total_population 0.004
## (0.007)
## date_2020_07:Total_population 0.002
## (0.007)
## date_2020_08:Total_population 0.004
## (0.007)
## date_2020_09:Total_population 0.007
## (0.008)
## date_2020_02:Ratio_of_aged_population -0.036
## (0.195)
## date_2020_03:Ratio_of_aged_population 0.002
## (0.248)
## date_2020_04:Ratio_of_aged_population -0.012
## (0.280)
## date_2020_05:Ratio_of_aged_population 0.014
## (0.297)
## date_2020_06:Ratio_of_aged_population 0.185
## (0.337)
## date_2020_07:Ratio_of_aged_population 0.239
## (0.346)
## date_2020_08:Ratio_of_aged_population 0.437
## (0.366)
## date_2020_09:Ratio_of_aged_population 0.633
## (0.420)
## as.factor(id)1:year_month_id 0.628 ***
## (0.122)
## as.factor(id)2:year_month_id 0.452 ***
## (0.073)
## as.factor(id)3:year_month_id 0.405 ***
## (0.055)
## as.factor(id)4:year_month_id 0.061
## (0.128)
## as.factor(id)5:year_month_id 0.258 *
## (0.115)
## as.factor(id)6:year_month_id 0.249
## (0.140)
## as.factor(id)7:year_month_id -0.112
## (0.121)
## as.factor(id)8:year_month_id -0.235 *
## (0.108)
## as.factor(id)9:year_month_id 0.628 ***
## (0.114)
## as.factor(id)10:year_month_id 0.139
## (0.150)
## as.factor(id)11:year_month_id 0.277 *
## (0.115)
## as.factor(id)12:year_month_id 0.041
## (0.168)
## as.factor(id)13:year_month_id -0.197
## (0.138)
## as.factor(id)14:year_month_id 0.083
## (0.134)
## as.factor(id)15:year_month_id 0.011
## (0.107)
## as.factor(id)16:year_month_id 0.411 *
## (0.180)
## as.factor(id)17:year_month_id 0.730 ***
## (0.174)
## as.factor(id)18:year_month_id 0.645 ***
## (0.137)
## as.factor(id)19:year_month_id -0.010
## (0.159)
## as.factor(id)20:year_month_id 0.455 **
## (0.156)
## as.factor(id)21:year_month_id 0.154
## (0.149)
## as.factor(id)22:year_month_id 0.413 *
## (0.156)
## as.factor(id)23:year_month_id 0.421 **
## (0.141)
## as.factor(id)24:year_month_id 0.602 ***
## (0.131)
## as.factor(id)25:year_month_id 0.412 **
## (0.137)
## as.factor(id)26:year_month_id 0.119
## (0.154)
## as.factor(id)27:year_month_id -0.007
## (0.122)
## as.factor(id)28:year_month_id -0.035
## (0.142)
## as.factor(id)29:year_month_id -0.722 ***
## (0.140)
## as.factor(id)30:year_month_id -0.618 ***
## (0.113)
## as.factor(id)31:year_month_id
##
## as.factor(id)32:year_month_id 0.628 ***
## (0.161)
## as.factor(id)33:year_month_id 0.263 **
## (0.081)
## as.factor(id)34:year_month_id 0.578 ***
## (0.144)
## as.factor(id)35:year_month_id -0.011
## (0.148)
## as.factor(id)36:year_month_id 0.808 ***
## (0.108)
## as.factor(id)37:year_month_id 0.031
## (0.124)
## as.factor(id)38:year_month_id 0.437 ***
## (0.065)
## as.factor(id)39:year_month_id 0.224 *
## (0.094)
## as.factor(id)40:year_month_id 0.530 **
## (0.166)
## as.factor(id)41:year_month_id 0.579 ***
## (0.099)
## as.factor(id)42:year_month_id 0.037
## (0.096)
## as.factor(id)43:year_month_id 1.212 ***
## (0.064)
## as.factor(id)44:year_month_id 1.168 ***
## (0.070)
## as.factor(id)45:year_month_id 0.984 ***
## (0.034)
## as.factor(id)46:year_month_id 1.084 ***
## (0.040)
## as.factor(id)47:year_month_id 0.592 **
## (0.190)
## --------------------------------------------------------------------
## R^2 0.955
## Adj. R^2 0.947
## Num. obs. 1551
## RMSE 144.465
## N Clusters 47
## ====================================================================
## *** p < 0.001; ** p < 0.01; * p < 0.05
# DID estimates and 90% CI
df_estimates <- DID_coefficients_main(DID_results = estimation_results,
treat_var = "unemploy_shock_diff2",
estimation_label = "yoy_hogo_households_WLS_trend")
# Event study graph
graph_yoy_hogo_households_WLS_trend_covar <- event_study_graph(data = df_estimates,
graph_title = "yoy_hogo_households_WLS_trend")
ggplotly(graph_yoy_hogo_households_WLS_trend_covar)